A bandit-based hyper-parameter optimization algorithm named Hyperband is proposed in [1]}. This algorithm is based on SuccessiveHalving [2]}, where a space of \(n\)  hyper-parameters is searched. At each iteration, all the hyper-parameters configurations are evaluated and the worst half are removed from further processing. This continues until only one configuration remains. Unlike SuccessiveHalving, in Hyperband, the value of \(n\)  is not pre-determined. This value is set according to the total processing budget and the maximum amount of resources that can be allocated to a particular allocation. In each iteration, \(n\)  is determined and then SuccessiveHalving is invoked to find the best hyper-parameters.
